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Problematic Internet Use (PIU) is increasingly recognized as a concern among internet users, prompting investigations into the factors that may predispose individuals to PIU. Interoceptive awareness, one of cognitive-perceptual factors, refers to the ability to perceive internal bodily sensations and has been shown to play a significant role in the onset and maintenance of drug addiction. However, its relationship with PIU remains underexplored. This study aimed to investigate the dimensions of interoceptive awareness within the context of PIU and to explore the role of personality traits in their relationships in a non-clinical sample of regular internet users. Involving 161 participants (71 males), the PIUQ-9 (Nine-Item Problematic Internet Use Questionnaire), DPIU (Dimensions of Problematic Internet Use), Neo-Pi-R NEO (Personality Inventory-Revised) and MAIA (Multidimensional Assessment of Interoceptive Awareness) questionnaires were employed. Spearman correlations and network analysis were conducted to assess relationships and interconnections among the variables. Neuroticism emerged as a central factor, strongly linked to both PIU and interoceptive awareness. Network analysis highlighted specific negative connections between the interoceptive states of Trusting and Not-Distracting and PIU. These preliminary findings suggest that certain interoceptive dimensions and personality traits, particularly neuroticism, are significantly associated with PIU. This study contributes to the field by highlighting interoceptive dimensions as relevant factors in understanding PIU and emphasizes the scarcity of research in this area, encouraging further investigation to address this gap.

A new method is proposed to determine the time–frequency content of time-dependent signals consisting of multiple oscillatory components, with time-varying amplitudes and instantaneous frequencies. Numerical experiments as well as a theoretical analysis are presented to assess its effectiveness.

Research on video gaming has been challenged by the way to properly measure individual play experience as a continuum, and current research primarily focuses on persons with gaming disorder based on the diagnostic criteria established in relation to substance use and gambling. To better capture the complexity and dynamic experience of gaming, an understanding of brain functional changes related to gaming is necessary. Based on the proinflammatory hypothesis of addiction, this scoping review was aiming to (1) survey the literature published since 2012 to determine how data pertinent to the measurement of stress response had been reported in video gaming studies and (2) clarify the link between gaming and stress response. Eleven studies were included in this review, and the results suggest that gaming could stimulate a stress-like physiological response, and the direction of this response is influenced by an individual’s biological profile, history of gaming, and gaming content. Our findings highlight the need for future investigation of the stress-behaviour correlation in the context of gaming, and this will assist in understanding the biological mechanisms underlying game addiction and inform the potential targets for addiction-related proinflammatory research.

- Basal cell carcinoma (BCC) is the most common human malignant neoplasm and is a frequently encountered diagnosis in dermatopathology. Although BCC may be locally destructive, it rarely metastasizes. Many diagnostic entities display morphologic and immunophenotypic overlap with BCC, including nonneoplastic processes, such as follicular induction over dermatofibroma; benign follicular tumors, such as trichoblastoma, trichoepithelioma, or basaloid follicular hamartoma; and malignant tumors, such as sebaceous carcinoma or Merkel cell carcinoma. Thus, misdiagnosis has significant potential to result in overtreatment or undertreatment. - To review key features distinguishing BCC from histologic mimics, including current evidence regarding immunohistochemical markers useful for that distinction. - Review of pertinent literature on BCC immunohistochemistry and differential diagnosis. - In most cases, BCC can be reliably diagnosed by histopathologic features. Immunohistochemistry may provide useful ancillary data in certain cases. Awareness of potential mimics is critical to avoid misdiagnosis and resulting inappropriate management.

AimMuch research has been conducted on the acute effects of nicotine on human cognitive performance, demonstrating both enhancing and impairing cognitive effects. With the relatively recent introduction of electronic cigarettes (‘e-cigarettes’) as a smoking cessation device, little is known about the cognitive effects of e-cigarettes specifically, either as a nicotine replacement device or in the absence of nicotine. The purpose of this review was to present an overview of evidence from empirical studies on the effect of e-cigarettes on cognitive function.ApproachGuided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews guidelines (PRISMA-ScR), SCOPUS, PubMed, and EBSCOhost were searched from 2006, the year e-cigarettes were introduced, to June 2023 for relevant papers, along with reference lists checked for additional papers.Key findingsSeven experimental and four cross-sectional survey studies were identified and included. The majority of the studies only include regular and current cigarette smokers and primarily assessed the acute cognitive effect of e-cigarettes relative to nicotine. While the findings primarily suggest either no or positive effect of e-cigarettes on cognition in cigarette smokers, associations between e-cigarettes and cognitive impairments in memory, concentration and decision making were reported in both cigarette smokers and never-smokers.Implications and conclusionsThe acute cognitive effect of e-cigarettes on regular cigarette smokers appears minimal. However, long-term cognitive effect and their effects on never-smokers are unclear. Given that the increased numbers of e-cigarette users are non-smokers and/or adolescents, research with those naïve to nicotine and a developmentally vulnerable adolescent population on its long-term effect is needed.

Wang examines the connection between innovation, education, and regional economic growth. Over the years, it's become clear that while technology has driven significant change, it alone isn't enough to ensure sustained regional economic growth. Success depends on a combination of dynamic research capacity, a diverse talent pool, innovation hubs, government support, and strong venture capital. University research has been central, with technology transfer offices helping commercialize academic inventions. However, commercialization is complex and requires long-term investment and collaboration between universities, industries, and governments. Federal initiatives like the NSF's Engineering Research Centers and the ARPA-E have played key roles in fostering innovation through long-term partnerships. State and local governments have also been essential in creating place-based innovation ecosystems, with examples like Silicon Valley and North Carolina's Research Triangle Park demonstrating the value of collaboration between universities, businesses, and local leaders. These efforts continue today with programs like the CHIPS and Science Act of 2022, which aim to expand innovation hubs and strengthen regional economies.

The paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spiking Neural Networks (SNN) architecture that enhances the model’s explainability while learning from streaming spatiotemporal brain data (STBD) in an incremental and on-line mode of operation. This led to the extraction of spatiotemporal rules from SNN models that explain why a certain decision (output prediction) was made by the model. During the learning process, the SNN created dynamic neural clusters, captured as polygons, which evolved in time and continuously changed their size and shape. The dynamic patterns of the clusters were quantitatively analyzed to identify the important STBD features that correspond to the most activated brain regions. We studied the trend of dynamically created clusters and their spike-driven events that occur together in specific space and time. The research contributes to: (1) enhanced interpretability of SNN learning behavior through dynamic neural clustering; (2) feature selection and enhanced accuracy of classification; (3) spatiotemporal rules to support model explainability; and (4) a better understanding of the dynamics in STBD in terms of feature interaction. The clustering method was applied to a case study of Electroencephalogram (EEG) data, recorded from a healthy control group (n = 21) and opiate use (n = 18) subjects while they were performing a cognitive task. The SNN models of EEG demonstrated different trends of dynamic clusters across the groups. This suggested to select a group of marker EEG features and resulted in an improved accuracy of EEG classification to 92%, when compared with all-feature classification. During learning of EEG data, the areas of neurons in the SNN model that form adjacent clusters (corresponding to neighboring EEG channels) were detected as fuzzy boundaries that explain overlapping activity of brain regions for each group of subjects.

Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that improve or control associated distress and anxiety. We propose a new Artificial Intelligence (AI) algorithm as a digital prognostic health system that models electroencephalographic (EEG) data in order to predict patients’ responses to tinnitus therapies. The EEG data was collected from patients prior to treatment and 3-months following a sound-based therapy. Feature selection techniques were utilised to identify predictive EEG variables with the best accuracy. The patients’ EEG features from both the frequency and functional connectivity domains were entered as inputs that carry knowledge extracted from EEG into AI algorithms for training and predicting therapy outcomes. The AI models differentiated the patients’ outcomes into either therapy responder or non-responder, as defined by their Tinnitus Functional Index (TFI) scores, with accuracies ranging from 98%–100%. Our findings demonstrate the potential use of AI, including deep learning, for predicting therapy outcomes in tinnitus. The research suggests an optimal configuration of the EEG sensors that are involved in measuring brain functional changes in response to tinnitus treatments. It identified which EEG electrodes are the most informative sensors and how the EEG frequency and functional connectivity can better classify patients into the responder and non-responder groups. This has potential for real-time monitoring of patient therapy outcomes at home.